!pip3 install plotly==4.14.1
import json
import plotly.graph_objects as go
import plotly.express as px
import plotly.offline as py
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=False)
import seaborn as sns
import matplotlib.pyplot as plt
import statsmodels.api as sm
import sklearn
import math
from datetime import datetime, date
from sklearn import preprocessing
from sklearn import datasets
from sklearn import utils
from sklearn import linear_model
from sklearn.metrics import *
from sklearn.preprocessing import *
from statsmodels.formula.api import ols
from sklearn.linear_model import LinearRegression
from sklearn.tree import DecisionTreeRegressor
from sklearn.neighbors import KNeighborsRegressor
from sklearn.model_selection import train_test_split
import pandas as pd
import numpy as np
facebook = pd.read_csv("data/Facebook.csv", sep=',')
apple = pd.read_csv("data/Apple.csv", sep=',')
amazon = pd.read_csv("data/Amazon.csv", sep=',')
netflix = pd.read_csv("data/Netflix.csv", sep=',')
google = pd.read_csv("data/Google.csv", sep=',')
facebook['Date'] = pd.to_datetime(facebook['Date'])
apple['Date'] = pd.to_datetime(apple['Date'])
amazon['Date'] = pd.to_datetime(amazon['Date'])
netflix['Date'] = pd.to_datetime(netflix['Date'])
google['Date'] = pd.to_datetime(google['Date'])
facebook = facebook[(facebook['Date'].dt.year > 2012) & (facebook['Date'].dt.year < 2021)]
apple = apple[(apple['Date'].dt.year > 2012) & (apple['Date'].dt.year < 2021)]
amazon = amazon[(amazon['Date'].dt.year > 2012) & (amazon['Date'].dt.year < 2021)]
netflix = netflix[(netflix['Date'].dt.year > 2012) & (netflix['Date'].dt.year < 2021)]
google = google[(google['Date'].dt.year > 2012) & (google['Date'].dt.year < 2021)]
facebook = facebook.reset_index(drop=True)
apple = apple.reset_index(drop=True)
amazon = amazon.reset_index(drop=True)
netflix = netflix.reset_index(drop=True)
google = google.reset_index(drop=True)
df_corr = pd.DataFrame()
df_corr['Facebook'] = facebook['Close']
df_corr['Apple'] = apple['Close']
df_corr['Amazon'] = amazon['Close']
df_corr['Netflix'] = netflix['Close']
df_corr['Google'] = google['Close']
retscomp = df_corr.pct_change()
corr = retscomp.corr()
corr
facebook['Company'] = ['Facebook']*len(facebook)
apple['Company'] = ['Apple']*len(apple)
amazon['Company'] = ['Amazon']*len(amazon)
netflix['Company'] = ['Netflix']*len(netflix)
google['Company'] = ['Google']*len(google)
frames = [facebook, apple, amazon, netflix, google]
result = pd.concat(frames)
fig = go.Figure()
fig.add_trace(go.Scatter(x=facebook.Date, y=facebook.Close, name='FB'))
fig.add_trace(go.Scatter(x=apple.Date, y=apple.Close, name='AAPL'))
fig.add_trace(go.Scatter(x=amazon.Date, y=amazon.Close, name='AMZN'))
fig.add_trace(go.Scatter(x=netflix.Date, y=netflix.Close, name='NFLX'))
fig.add_trace(go.Scatter(x=google.Date, y=google.Close, name='GOOG'))
fig.update_layout(title='Close prices for All Companies from Jan 2016 to Dec 2019',
xaxis_title='Date',
yaxis_title='Close Price')
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Facebook',
method = 'update',
args = [{'visible': [True, False, False, False, False]},
{'title': 'FB',
'showlegend':True}]),
dict(label = 'Apple',
method = 'update',
args = [{'visible': [False, True, False, False, False]},
{'title': 'APPL',
'showlegend':True}]),
dict(label = 'Amazon',
method = 'update',
args = [{'visible': [False, False, True, False, False]},
{'title': 'AMZN',
'showlegend':True}]),
dict(label = 'Netflix',
method = 'update',
args = [{'visible': [False, False, False, True, False]},
{'title': 'NFLX',
'showlegend':True}]),
dict(label = 'Google',
method = 'update',
args = [{'visible': [False, False, False, False, True]},
{'title': 'GOOG',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
fig = px.imshow(corr)
iplot(fig,show_link=False)
avg_14 = facebook.Close.rolling(window=14, min_periods=1).mean()
avg_21 = facebook.Close.rolling(window=21, min_periods=1).mean()
avg_100 = facebook.Close.rolling(window=100, min_periods=1).mean()
x_fb = facebook['Date']
y_fb = facebook['Open']
z_fb = facebook['Close']
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_fb, y=y_fb, name='Open',
line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=z_fb, name = 'Close',
line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
line=dict(color='mediumorchid', width=1.5)))
fig.update_layout(title='Open and Close prices for Facebook from Jan 2016 to Dec 2019',
xaxis_title='Date',
yaxis_title='Open/Close Price')
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Open Price',
method = 'update',
args = [{'visible': [True, False, False, False, False]},
{'title': 'Open Price',
'showlegend':True}]),
dict(label = 'Close Price',
method = 'update',
args = [{'visible': [False, True, False, False, False]},
{'title': 'Close Price',
'showlegend':True}]),
dict(label = '14 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, True, False, False]},
{'title': '14 Day Moving Average',
'showlegend':True}]),
dict(label = '21 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, True, False]},
{'title': '21 Day Moving Average',
'showlegend':True}]),
dict(label = '100 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, False, True]},
{'title': '100 Day Moving Average',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
avg_14 = apple.Close.rolling(window=14, min_periods=1).mean()
avg_21 = apple.Close.rolling(window=21, min_periods=1).mean()
avg_100 = apple.Close.rolling(window=100, min_periods=1).mean()
x_ap = apple['Date']
y_ap = apple['Open']
z_ap = apple['Close']
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_ap, y=y_ap, name='Open',
line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_ap, y=z_ap, name = 'Close',
line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
line=dict(color='mediumorchid', width=1.5)))
fig.update_layout(title='Open and Close prices for Apple from Jan 2016 to Dec 2019',
xaxis_title='Date',
yaxis_title='Open/Close Price')
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Open Price',
method = 'update',
args = [{'visible': [True, False, False, False, False]},
{'title': 'Open Price',
'showlegend':True}]),
dict(label = 'Close Price',
method = 'update',
args = [{'visible': [False, True, False, False, False]},
{'title': 'Close Price',
'showlegend':True}]),
dict(label = '14 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, True, False, False]},
{'title': '14 Day Moving Average',
'showlegend':True}]),
dict(label = '21 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, True, False]},
{'title': '21 Day Moving Average',
'showlegend':True}]),
dict(label = '100 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, False, True]},
{'title': '100 Day Moving Average',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
avg_14 = amazon.Close.rolling(window=14, min_periods=1).mean()
avg_21 = amazon.Close.rolling(window=21, min_periods=1).mean()
avg_100 = amazon.Close.rolling(window=100, min_periods=1).mean()
x_am = amazon['Date']
y_am = amazon['Open']
z_am = amazon['Close']
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_am, y=y_am, name='Open',
line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_am, y=z_am, name = 'Close',
line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
line=dict(color='mediumorchid', width=1.5)))
fig.update_layout(title='Open and Close prices for Amazon from Jan 2016 to Dec 2019',
xaxis_title='Date',
yaxis_title='Open/Close Price')
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Open Price',
method = 'update',
args = [{'visible': [True, False, False, False, False]},
{'title': 'Open Price',
'showlegend':True}]),
dict(label = 'Close Price',
method = 'update',
args = [{'visible': [False, True, False, False, False]},
{'title': 'Close Price',
'showlegend':True}]),
dict(label = '14 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, True, False, False]},
{'title': '14 Day Moving Average',
'showlegend':True}]),
dict(label = '21 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, True, False]},
{'title': '21 Day Moving Average',
'showlegend':True}]),
dict(label = '100 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, False, True]},
{'title': '100 Day Moving Average',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
avg_14 = netflix.Close.rolling(window=14, min_periods=1).mean()
avg_21 = netflix.Close.rolling(window=21, min_periods=1).mean()
avg_100 = netflix.Close.rolling(window=100, min_periods=1).mean()
x_ne = netflix['Date']
y_ne = netflix['Open']
z_ne = netflix['Close']
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_ne, y=y_ne, name='Open',
line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_ne, y=z_ne, name = 'Close',
line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
line=dict(color='mediumorchid', width=1.5)))
fig.update_layout(title='Open and Close prices for Netflix from Jan 2016 to Dec 2019',
xaxis_title='Date',
yaxis_title='Open/Close Price')
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Open Price',
method = 'update',
args = [{'visible': [True, False, False, False, False]},
{'title': 'Open Price',
'showlegend':True}]),
dict(label = 'Close Price',
method = 'update',
args = [{'visible': [False, True, False, False, False]},
{'title': 'Close Price',
'showlegend':True}]),
dict(label = '14 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, True, False, False]},
{'title': '14 Day Moving Average',
'showlegend':True}]),
dict(label = '21 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, True, False]},
{'title': '21 Day Moving Average',
'showlegend':True}]),
dict(label = '100 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, False, True]},
{'title': '100 Day Moving Average',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
avg_14 = google.Close.rolling(window=14, min_periods=1).mean()
avg_21 = google.Close.rolling(window=21, min_periods=1).mean()
avg_100 = google.Close.rolling(window=100, min_periods=1).mean()
x_go = google['Date']
y_go = google['Open']
z_go = google['Close']
fig = go.Figure()
fig.add_trace(go.Scatter(x=x_go, y=y_go, name='Open',
line=dict(color='royalblue', width=1.5)))
fig.add_trace(go.Scatter(x=x_go, y=z_go, name = 'Close',
line=dict(color='firebrick', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_14, name = '14 Day Close Avg',
line=dict(color='gold', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_21, name = '21 Day Close Avg',
line=dict(color='orangered', width=1.5)))
fig.add_trace(go.Scatter(x=x_fb, y=avg_100, name = '100 Day Close Avg',
line=dict(color='mediumorchid', width=1.5)))
fig.update_layout(title='Open and Close prices for Google from Jan 2016 to Dec 2019',
xaxis_title='Date',
yaxis_title='Open/Close Price')
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Open Price',
method = 'update',
args = [{'visible': [True, False, False, False, False]},
{'title': 'Open Price',
'showlegend':True}]),
dict(label = 'Close Price',
method = 'update',
args = [{'visible': [False, True, False, False, False]},
{'title': 'Close Price',
'showlegend':True}]),
dict(label = '14 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, True, False, False]},
{'title': '14 Day Moving Average',
'showlegend':True}]),
dict(label = '21 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, True, False]},
{'title': '21 Day Moving Average',
'showlegend':True}]),
dict(label = '100 Day Moving Average',
method = 'update',
args = [{'visible': [False, False, False, False, True]},
{'title': '100 Day Moving Average',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
avg_close = result.groupby('Date')['Close'].mean()
stand_close = result.groupby('Date')['Close'].std()
stand_close = stand_close.reset_index()
avg_close = avg_close.reset_index()
result['standard_close'] = np.arange(len(result.index))
result = result.reset_index(drop=True)
for x, rows in result.iterrows():
result.loc[x, 'standard_close'] = (rows['Close'] - avg_close[avg_close['Date'] == rows['Date']]['Close']).values/(stand_close[stand_close['Date'] == rows['Date']]['Close']).values
result
fig = px.line(result, x="Date", y="standard_close", color='Company')
fig.update_layout(title='Standardized Close prices for All Companies from Jan 2016 to Dec 2019',
xaxis_title='Date',
yaxis_title='Standardized Close Price')
iplot(fig,show_link=False)
df = facebook[['Close']].copy(deep=True)
future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)
X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)
x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days)
x_future = np.array(x_future)
tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)
predictions = tree_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig = go.Figure()
fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
line=dict(width=1.5)))
predictions = lr_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
line=dict(width=1.5)))
predictions = knn_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
line=dict(width=1.5)))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Decision Tree Prediction',
method = 'update',
args = [{'visible': [True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'Linear Regression Prediction',
method = 'update',
args = [{'visible': [True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'k-NN Regressor Prediction',
method = 'update',
args = [{'visible': [True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
df = apple[['Close']].copy(deep=True)
future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)
X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)
x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days)
x_future = np.array(x_future)
tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)
predictions = tree_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig = go.Figure()
fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
line=dict(width=1.5)))
predictions = lr_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
line=dict(width=1.5)))
predictions = knn_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
line=dict(width=1.5)))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Decision Tree Prediction',
method = 'update',
args = [{'visible': [True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'Linear Regression Prediction',
method = 'update',
args = [{'visible': [True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'k-NN Regressor Prediction',
method = 'update',
args = [{'visible': [True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
df = amazon[['Close']].copy(deep=True)
future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)
X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)
x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days)
x_future = np.array(x_future)
tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)
predictions = tree_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig = go.Figure()
fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
line=dict(width=1.5)))
predictions = lr_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
line=dict(width=1.5)))
predictions = knn_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
line=dict(width=1.5)))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Decision Tree Prediction',
method = 'update',
args = [{'visible': [True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'Linear Regression Prediction',
method = 'update',
args = [{'visible': [True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'k-NN Regressor Prediction',
method = 'update',
args = [{'visible': [True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
df = netflix[['Close']].copy(deep=True)
future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)
X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)
x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days)
x_future = np.array(x_future)
tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)
predictions = tree_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig = go.Figure()
fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
line=dict(width=1.5)))
predictions = lr_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
line=dict(width=1.5)))
predictions = knn_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
line=dict(width=1.5)))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Decision Tree Prediction',
method = 'update',
args = [{'visible': [True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'Linear Regression Prediction',
method = 'update',
args = [{'visible': [True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'k-NN Regressor Prediction',
method = 'update',
args = [{'visible': [True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
df = google[['Close']].copy(deep=True)
future_days = 500
df['Prediction'] = df[['Close']].shift(-future_days)
X = np.array(df.drop(['Prediction'], 1))[:-future_days]
y = np.array(df['Prediction'])[:-future_days]
x_train, x_test, y_train, y_test = train_test_split(X, y, test_size = 0.25)
tree = DecisionTreeRegressor().fit(x_train, y_train)
lr = LinearRegression().fit(x_train, y_train)
knn = KNeighborsRegressor().fit(x_train, y_train)
x_future = df.drop(['Prediction'], 1)[:-future_days]
x_future = x_future.tail(future_days)
x_future = np.array(x_future)
tree_prediction = tree.predict(x_future)
lr_prediction = lr.predict(x_future)
knn_prediction = knn.predict(x_future)
predictions = tree_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig = go.Figure()
fig.add_trace(go.Scatter(x=df.index.values, y=df['Close'], name='Actual Close',
line=dict(width=1.5)))
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close D-Tree',
line=dict(width=1.5)))
predictions = lr_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close Lin Reg',
line=dict(width=1.5)))
predictions = knn_prediction
valid = df[X.shape[0]:].copy(deep=True)
valid['Predictions'] = predictions
valid[['Close','Predictions']]
fig.add_trace(go.Scatter(x=valid.index.values, y=valid['Predictions'], name='Predicted Close k-NN',
line=dict(width=1.5)))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Decision Tree Prediction',
method = 'update',
args = [{'visible': [True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'Linear Regression Prediction',
method = 'update',
args = [{'visible': [True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'k-NN Regressor Prediction',
method = 'update',
args = [{'visible': [True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
facebook['timestamp'] = pd.to_datetime(facebook.Date).astype(int) // (10**9)
X = np.array(facebook['timestamp']).reshape(-1,1)
y = np.array(facebook['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig = go.Figure()
fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))
model = KNeighborsRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))
model = DecisionTreeRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Linear Regression',
method = 'update',
args = [{'visible': [True, True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'k-NN Regressor',
method = 'update',
args = [{'visible': [True, True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'Decision Tree Regressor',
method = 'update',
args = [{'visible': [True, True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
apple['timestamp'] = pd.to_datetime(apple.Date).astype(int) // (10**9)
X = np.array(facebook['timestamp']).reshape(-1,1)
y = np.array(facebook['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig = go.Figure()
fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))
model = KNeighborsRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))
model = DecisionTreeRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Linear Regression',
method = 'update',
args = [{'visible': [True, True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'k-NN Regressor',
method = 'update',
args = [{'visible': [True, True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'Decision Tree Regressor',
method = 'update',
args = [{'visible': [True, True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
amazon['timestamp'] = pd.to_datetime(amazon.Date).astype(int) // (10**9)
X = np.array(facebook['timestamp']).reshape(-1,1)
y = np.array(facebook['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig = go.Figure()
fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))
model = KNeighborsRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))
model = DecisionTreeRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Linear Regression',
method = 'update',
args = [{'visible': [True, True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'k-NN Regressor',
method = 'update',
args = [{'visible': [True, True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'Decision Tree Regressor',
method = 'update',
args = [{'visible': [True, True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
netflix['timestamp'] = pd.to_datetime(netflix.Date).astype(int) // (10**9)
X = np.array(facebook['timestamp']).reshape(-1,1)
y = np.array(facebook['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig = go.Figure()
fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))
model = KNeighborsRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))
model = DecisionTreeRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Linear Regression',
method = 'update',
args = [{'visible': [True, True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'k-NN Regressor',
method = 'update',
args = [{'visible': [True, True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'Decision Tree Regressor',
method = 'update',
args = [{'visible': [True, True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)
google['timestamp'] = pd.to_datetime(google.Date).astype(int) // (10**9)
X = np.array(facebook['timestamp']).reshape(-1,1)
y = np.array(facebook['Close'])
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
model = LinearRegression()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig = go.Figure()
fig.add_trace(go.Scatter(x=X_train.squeeze(), y=y_train, name='Training Data', mode='markers'))
fig.add_trace(go.Scatter(x=X_test.squeeze(), y=y_test, name='Testing Data', mode='markers'))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Linear Regression'))
model = KNeighborsRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='kNN Regressor'))
model = DecisionTreeRegressor()
model.fit(X_train, y_train)
x_range = np.linspace(X.min(), X.max(), 100)
y_range = model.predict(x_range.reshape(-1, 1))
fig.add_trace(go.Scatter(x=x_range, y=y_range, name='Decision Tree'))
fig.update_layout(
updatemenus=[
dict(
buttons=list([
dict(label = 'All',
method = 'update',
args = [{'visible': [True, True, True, True, True]},
{'title': 'All',
'showlegend':True}]),
dict(label = 'Linear Regression',
method = 'update',
args = [{'visible': [True, True, True, False, False]},
{'title': 'Linear Regression',
'showlegend':True}]),
dict(label = 'k-NN Regressor',
method = 'update',
args = [{'visible': [True, True, False, True, False]},
{'title': 'k-NN Regressor',
'showlegend':True}]),
dict(label = 'Decision Tree Regressor',
method = 'update',
args = [{'visible': [True, True, False, False, True]},
{'title': 'Decision Tree Regressor',
'showlegend':True}]),
]),
direction="down",
pad={"r": 10, "t": 10},
showactive=True,
x=0.1,
xanchor="left",
y=1.1,
yanchor="top"
),
]
)
fig.update_layout(
autosize=False,
width=1000,
height=650,)
iplot(fig,show_link=False)